CN114708990B - Remote digital media sharing method and system based on 5G internet - Google Patents

Remote digital media sharing method and system based on 5G internet Download PDF

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CN114708990B
CN114708990B CN202210632168.9A CN202210632168A CN114708990B CN 114708990 B CN114708990 B CN 114708990B CN 202210632168 A CN202210632168 A CN 202210632168A CN 114708990 B CN114708990 B CN 114708990B
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缪培生
刘士远
萧毅
杨志胤
陈晓峰
朱余明
常祥锋
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Lung Diagnosis Network Suzhou Network Technology Co ltd
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Abstract

The invention relates to a remote digital media sharing method and a system based on a 5G internet, wherein the method comprises the following steps of S1: collecting personal health information; step S2: the local node manages personal health information and sends the personal health information to the remote node; step S3: the remote node collects and stores the received personal health information to a remote storage node; step S4: the remote node provides a sharing service of the personal health information upon receiving a remote access request of the local node. The identification combination obtained by calculation of different hash functions forms data desensitization and data barriers to different local nodes, and meanwhile, the searching efficiency is accelerated; the personal health information associated with the sensitive field is provided only on the basis that the local node itself already possesses the information contained in the sensitive field, thereby desensitizing the lack of possession of the personal health information of the new local node contained in the sensitive field.

Description

Remote digital media sharing method and system based on 5G internet
Technical Field
The invention belongs to the technical field of 5G internet, and particularly relates to a remote digital media sharing method and system based on the 5G internet.
Background
The fifth generation mobile communication technology is the latest generation cellular mobile communication technology, also an extension behind the 4G (LTE-A, WiMax), 3G (UMTS, LTE) and 2G (gsm) systems. The performance goals of 5G are high data rates, reduced latency, energy savings, reduced cost, increased system capacity, and large-scale device connectivity. The main advantage of 5G networks is that the data transmission rate is much higher than in previous cellular networks, up to 10Gbit/s, 100 times faster than in 4G lte cellular networks. There is a lower network delay (faster response time) of less than 1 millisecond and 4G of 30-70 milliseconds. Since the data transfer is faster. The 5G network provides high-speed, low-delay and large-capacity technical support for realizing cloud-to-end mass image data transmission, health portrait AI quantitative cloud computing, mobile CT diagnosis and treatment vehicle business development, family and wearable health monitoring and intelligent mobile phone personal health connection management, and can realize application of chest health continuous management service modes of personal accurate health portrait, hospital classified diagnosis and treatment and health management, screening and post-examination management of mobile CT vehicles and chain physical examination mechanisms and family cardiopulmonary health monitoring.
The three-level linkage of province, city and county across areas, the examination and diagnosis linkage of a physical examination center and a hospital, and the doctor-patient interaction of doctors and patients are all carried out on the basis of the Internet. The safety protection requirements, the rights and interests protection requirements and the patient privacy protection requirements of all levels of hospitals on the original image data are very urgent tasks of the current intelligent medical health services. The existing medical information systems, especially medical image transmission and storage systems, are all centralized storage modes. For a cross-regional, networked and SaaS-based chest major chronic disease early screening early diagnosis system, a centralized storage system cannot meet the requirements of three-level linkage, examination and diagnosis linkage and doctor-patient interaction, and a centralized distributed storage or centralized and distributed mixed medical image sharing cooperative system is urgently needed. Under the circumstance, how to provide remote digital media sharing based on the advantages of the 5G internet, thereby supporting the construction of medical health integrated service sharing, is a problem to be solved. The identification combination obtained by calculation of different hash functions forms data desensitization and data barriers to different local nodes, and meanwhile, the searching efficiency is accelerated; the personal health information associated with the sensitive field is provided only on the basis that the local node itself already possesses the information contained in the sensitive field, thereby desensitizing the lack of possession of the personal health information of the new local node contained in the sensitive field.
Disclosure of Invention
In order to solve the above problems in the prior art, the present invention provides a remote digital media sharing method and system based on the 5G internet, wherein the method comprises:
step S1: collecting personal health information;
step S2: the local node manages personal health information and sends the personal health information to the remote node;
the local node performs personal health information management, and specifically comprises the following steps:
step SA 21: determining sensitive fields in the collected personal health information; wherein: the sensitive field is a field capable of personal identification;
step SA 22: acquiring a corresponding hash function according to the identification of the local control node; different local control nodes adopt different hash functions, the different hash functions are advanced for the current local node and the remote node, and the hash functions of the current local node are unknown by other local nodes;
step SA 23: respectively calculating the hash value of each sensitive field and the specific sensitive field combination by adopting a hash function; wherein: the particular sensitive field combination comprises a plurality of sensitive fields; the specific sensitive field and the specific sensitive field combination are preset;
step SA 24: searching an identification combination of the local storage node by adopting a hash value of a specific sensitive field or a specific sensitive field combination, if the identification combination is hit, acquiring a corresponding record, and updating the personal health information stored in the local storage node by using the acquired personal health information; otherwise, the combination of the hash values of the sensitive fields is used as an identification combination, a new health record is created, and the personal health information and the identification combination are stored in the local storage node in an associated manner;
the combination hit with the identifier specifically includes: when one or more of the particular sensitive fields, or one or more of the particular combinations of sensitive fields, hit one or more corresponding sensitive fields, or combinations of sensitive fields, of the identified combinations hit;
step S3: the remote node collects and stores the received personal health information to a remote storage node;
the step S3 specifically includes the following steps:
step S31: determining sensitive fields in the personal health information;
step S32: acquiring a corresponding hash function according to the identification of the local control node sending the personal health information; specifically, the method comprises the following steps: acquiring an ith hash function corresponding to the ith local control node;
step S33: respectively calculating the hash value of each sensitive field by adopting an ith hash function; information collection is given up in a sensitive field combination mode when the remote node stores, and the reliability of data collected by the remote node is improved;
step S34: calculating the credibility score of each piece of personal health information, and updating the personal health information record with the highest credibility score by using the received personal health information; wherein i is the number of the local control node;
step S4: the remote node provides a sharing service of the personal health information upon receiving a remote access request of the local node.
Further, the step S34 specifically includes the following steps:
step S341: respectively searching remote storage nodes based on the hash value of each sensitive field to acquire personal health data hit by any hash value as personal health information to be updated;
step S342: acquiring a piece of personal health information to be updated;
step S343: calculating a credibility score corresponding to each hash value through a mapping function; wherein: the trustworthiness score comprises a conditional trustworthiness score and an unconditional trustworthiness score; the mapping function is set in a mapping table mode;
calculating a conditional confidence score corresponding to each hash value
Figure 142063DEST_PATH_IMAGE001
The method specifically comprises the following steps:
Figure 692124DEST_PATH_IMAGE002
；
Figure 808985DEST_PATH_IMAGE003
when the sensitive field i is matched and the sensitive field or the sensitive field set map (i) is matched, the hash value corresponding to the sensitive field i
Figure 786299DEST_PATH_IMAGE004
A corresponding confidence score;
Figure 496766DEST_PATH_IMAGE005
represents the corresponding hash value or hash value set of the sensitive field or sensitive field set map (i);
calculating an unconditional confidence score corresponding to each hash value
Figure 630944DEST_PATH_IMAGE006
The method specifically comprises the following steps:
Figure 743257DEST_PATH_IMAGE007
when the sensitive field i is matched, the hash value corresponding to the sensitive field i
Figure 422631DEST_PATH_IMAGE008
A corresponding confidence score;
step S344: calculating a total credibility score of the personal health information to be updated
Figure 620394DEST_PATH_IMAGE009
；
Figure 292684DEST_PATH_IMAGE010
；
Step S345: judging whether all the personal health information to be updated is processed, if so, updating the personal health information record corresponding to the personal health information to be updated with the highest total credibility score by using the received personal health information; otherwise, the combination of the hash values is used as an identification combination, a new health record is created, and the personal health information and the identification combination are stored in the local storage node in an associated mode.
Furthermore, the sensitive fields are identification number, name, diagnosis and treatment number and mobile phone number.
Further, the specific sensitive fields are combined into one or more fields.
Further, the local storage node stores the personal health information in a classified manner.
Based on the same inventive concept, the invention also provides a remote digital media sharing system based on the 5G internet, which comprises: a remote node and a local node; the remote nodes comprise a remote control node and a remote storage node; the local nodes comprise local control nodes and local storage nodes; the remote node and the local node communicate with each other through a 5G internet.
Further, the local node is used for collecting the personal health information from the local and storing the personal health information in the local storage node; the system is also used for transmitting the collected personal health information to a remote node through the 5G internet; the local node is further configured to obtain the personal health information from the remote node and provide health services based on the obtained personal health information.
Based on the same inventive concept, the invention further provides a processor for executing the program, wherein the program executes the remote digital media sharing method based on the 5G internet.
Based on the same inventive concept, the present invention also provides a computer-readable storage medium including a program, which, when run on a computer, causes the computer to perform the 5G internet-based remote digital media sharing method.
Based on the same inventive concept, the present invention also provides an executing device, which comprises a processor, the processor is coupled with a memory, the memory stores program instructions, and when the program instructions stored in the memory are executed by the processor, the executing device realizes the remote digital media sharing method based on the 5G internet.
The beneficial effects of the invention include:
(1) the gathering capacity is greatly expanded on the basis of not reducing the availability and credibility of information by a specific sensitive field combination mode, and personal health information in different credibility ranges is formed between the local nodes; (2) the remote node does not need to carry out data modification and desensitization calculation, data desensitization and data barriers to different local nodes are formed by identification combinations obtained by calculation of different hash functions, and meanwhile, the searching efficiency is accelerated; (3) based on a mapping function with conditional credibility and unconditionalcredibility, the credible scores corresponding to the hash values are quantitatively calculated, and the personal health information acquired by the complex data source is subjected to convergent updating, so that the reliable information is finally completed, and the unreliable or wrong personal health information is gradually marginalized and finally deleted, thereby realizing the information optimization of the remote storage node; when desensitization is formed by using the hash value, hierarchical access and management of quantization of data are realized by combining the means of field combination, and the efficiency of data sharing is improved; (4) the personal health information associated with the sensitive field is provided only on the basis that the local node itself already possesses the information contained in the sensitive field, thereby desensitizing the lack of possession of the personal health information of the new local node contained in the sensitive field.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, and are not to be considered limiting of the invention, in which:
fig. 1 is a schematic diagram of a remote digital media sharing method based on the 5G internet according to the present invention.
Detailed Description
The present invention will now be described in detail with reference to the drawings and specific embodiments, wherein the exemplary embodiments and descriptions are only intended to illustrate the present invention, but not to limit the present invention.
The invention provides a remote digital media sharing system based on a 5G internet, which comprises: a remote node and a local node; the remote nodes comprise a remote control node and a remote storage node; the local nodes comprise local control nodes and local storage nodes; the remote node and the local node communicate through a 5G internet;
the local node is used for collecting personal health information from the local and storing the personal health information in a local storage node; the system is also used for transmitting the collected personal health information to a remote node through the 5G internet; the local node is also used for acquiring the personal health information from the remote node and providing health service based on the acquired personal health information;
the remote node receives and collects the personal health information from different local nodes, desensitizes the personal health information, and stores the desensitized personal health information in the remote storage node;
preferably: the local medical institutions comprise secondary and tertiary hospitals, primary hospitals, civil hospitals or physical examination centers and the like; the number of the local nodes is one or more, the local nodes serve one or more corresponding local medical institutions respectively, the data collected and stored in each local node is only used by the local node and is not accessible to other local nodes, and therefore privacy of non-desensitized data is guaranteed and personal health information is spread in different credible ranges; personal health information stored on the remote node is high in credibility and can be transmitted among different local nodes, and personal health information stored on the local node is low in credibility and can gather more personal health information;
preferably: the local node is used for acquiring desensitized personal health information from the remote node in a remote access mode;
preferably: the remote node provides an interface, and the local node accesses the remote node in a remote mode through the interface;
the remote nodes form a basic computing resource platform, a 5G and Unicom cloud computing data center is adopted to construct a system matched construction and realize the basic computing resource platform, the unified computing, storage, centralized management and backup of all data of the system are supported, the unified monitoring, operation and maintenance and safety are realized, a cloud data center with high performance, high reliability and strong flexibility is realized, and the informationized unified computing, unified storage and unified monitoring operation and maintenance are realized; sensitive data are guaranteed not to be out of the local province, not out of the local organization, and only the desensitized data are sent out, so that the privacy of personal health data of screening personnel is protected;
preferably: the remote control nodes exist in the form of a cluster, which is a logical collection of a set of physical hosts (compute nodes); the physical host provides resources such as computing, network, storage and the like for the cloud host instance;
the remote storage node is used for storing a storage server of a disk file (including a root cloud disk, a data cloud disk, a root cloud disk snapshot, a data cloud disk snapshot, a mirror image cache and the like) of the cloud host; supporting local storage, NFS, SharedMountPoint, SharedLock, Ceph types;
preferably, the following components: the remote node is also provided with a mirror image server, a storage server for storing mirror image templates and supporting mirror image warehouses, Sftp types and Ceph types;
as shown in fig. 1, the present invention provides a remote digital media sharing method based on 5G internet, the method includes the following steps:
step S1: collecting personal health information; the method specifically comprises the following steps: the local node acquires personal information through personal health acquisition equipment;
based on the heterogeneous health data automatic extraction technology, the method supports various acquisition modes such as timing acquisition, real-time acquisition, full-scale extraction, incremental extraction, data inspection, master-slave extraction and multi-table combined extraction, and realizes that the multi-source, multi-dimensional, real-time, continuous, dispersive, dynamic and heterogeneous personal health information is converged to a remote node for centralized storage and management; here, ETL (Extract-Transform-Load) is adopted for timing data acquisition aiming at data with low real-time requirement of each mechanism service system, so that zero influence on mechanism services can be realized;
step S2: the local node manages personal health information and sends the personal health information to the remote node; the method specifically comprises the following steps: after the local node collects the personal health information, a personal health information record is created for each piece of personal health information, and the personal health information record is stored in a local storage node;
preferably: taking the personal health information of each individual as a piece of personal health information, and correspondingly creating a piece of personal health information record;
the local node specifically performs personal health information management, and comprises the following steps:
step SA 21: determining sensitive fields in the collected personal health information; wherein: the sensitive field is a field capable of personal identification; for example: identification card number, name, diagnosis and treatment number, mobile phone number and the like;
alternatively: the information corresponding to the sensitive field is the information acquired by the limited user; that is, the information is invisible to a part of the users;
step SA 22: acquiring a corresponding hash function according to the identification of the local control node; different local control nodes adopt different hash functions, the different hash functions are predetermined for the current local node and the remote node, and the hash functions of other local nodes for the current local node are unknown;
step SA 23: respectively calculating the hash value of each sensitive field and the specific sensitive field combination by adopting a hash function; wherein: the particular sensitive field combination comprises a plurality of sensitive fields; the specific sensitive field and the specific sensitive field combination are preset;
preferably: the specific sensitive fields are combined into one or more fields;
step SA 24: searching an identification combination of the local storage node by using the hash value of the specific sensitive field or the specific sensitive field combination, if the identification combination is hit, acquiring a corresponding record, and updating the personal health information stored in the local storage node by using the acquired personal health information; otherwise, the combination of the hash values of the sensitive fields is used as an identification combination, a new health record is created, and the personal health information and the identification combination are stored in a local storage node in an associated manner;
that is, each piece of personal health information corresponds to an identification combination, and the identification combination is a multi-tuple, wherein each tuple corresponds to a hash value of a sensitive field, that is, a quick search for a record can be formed for each sensitive field; when a part of sensitive field values in one piece of stored personal health information are missing, the hash values corresponding to the sensitive fields are also missing; when information is updated, updating personal health information, and simultaneously, synchronously updating the identification combination for the tuple corresponding to the newly added sensitive field value;
the combination hit with the identifier specifically includes: when one or more of the particular sensitive fields, or one or more of the particular combinations of sensitive fields, hit one or more corresponding sensitive fields, or combinations of sensitive fields, of the identified combinations hit; that is, although there may be a plurality of sensitive fields and a plurality of sensitive field combinations, the specific sensitive field and the specific sensitive field combination are fields that can clearly identify the record; a field with a certain identification threshold;
when one sensitive field cannot form the identification capability of the individual, the combination of a plurality of sensitive fields can form the identification capability of the individual; for example: the name + mobile phone number can form strong personal identification capability; age plus phone number can also form stronger identification capability; name + age can form a certain identification capability; the local storage plays a role of collecting a large amount of data, local nodes need to ensure safe, standard and high-quality collection and storage of multi-source data, and the collection capacity is greatly expanded on the basis of not reducing the availability and credibility of information in a specific sensitive field combination mode;
the combination hit with the identifier specifically includes: each sensitive field and each sensitive field combination respectively correspond to a credible value, the credibility of each specific sensitive field or each specific sensitive field combination is greater than a first credible value, and when the hash value of any one sensitive field or any one sensitive field combination hits any one element in the identification combination, the identification combination is determined to hit;
preferably: the first credible value is a preset value;
the sending of the personal health information to the remote node specifically comprises: sending the record corresponding to the acquired personal health information to a remote node; preferably: at this time, no identifier combination is sent;
preferably: when the sending opportunity arrives, sending the personal health information collected between the current sending opportunity and the last sending opportunity to a remote node; for example: sending personal health information at certain time intervals;
preferably: the method is characterized in that the personal health information is classified and stored, a plurality of standardized data centers which can adapt to dynamic increase of data demand are constructed, and the method comprises the following steps: a health archive center (EHR), a clinical information center (EMR), a health monitoring and controlling data center and the like form a unified personal data classification management system to ensure that the quality and the availability of personal health information reach the standard;
step S3: the remote node collects and stores the received personal health information to a remote storage node; the step S3 specifically includes the following steps:
step S31: determining sensitive fields in the personal health information;
step S32: acquiring a corresponding hash function according to the identification of the local control node sending the personal health information; specifically, the method comprises the following steps: acquiring an ith hash function corresponding to the ith local control node;
step S33: respectively calculating the hash value of each sensitive field by adopting an ith hash function; when the field value of one sensitive field does not exist, the sensitive field cannot acquire an effective hash value, and naturally cannot contribute to the credible score; when the remote node stores the data, the information collection is abandoned in a sensitive field combination mode, and the reliability of the data collected by the remote node is improved;
step S34: calculating the credibility score of each piece of personal health information, and updating the personal health information record with the highest credibility score by using the received personal health information; wherein i is the number of the local control node;
the step S34 specifically includes the following steps:
step S341: respectively searching remote storage nodes based on the hash value of each sensitive field to acquire personal health data hit by any hash value as personal health information to be updated;
step S342: acquiring a piece of personal health information to be updated;
step S343: calculating a credibility score corresponding to each hash value through a mapping function; wherein: the trustworthiness score comprises a conditional trustworthiness score and an unconditional trustworthiness score; the mapping function is set in a mapping table mode;
calculating a conditional confidence score corresponding to each hash value
Figure 100002_DEST_PATH_IMAGE011
The method specifically comprises the following steps:
Figure 806973DEST_PATH_IMAGE012
；
Figure DEST_PATH_IMAGE013
when the sensitive field i is matched and the sensitive field or the sensitive field set map (i) is matched, the hash value corresponding to the sensitive field i
Figure 640937DEST_PATH_IMAGE014
A corresponding confidence score;
Figure DEST_PATH_IMAGE015
represents the corresponding hash value or hash value set of the sensitive field or sensitive field set map (i); that is, i can obtain its corresponding score only if the sensitive field forming conditional confidence with i is also matched;
calculating an unconditional trusted score corresponding to each hash value
Figure 404625DEST_PATH_IMAGE016
The method specifically comprises the following steps:
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when the sensitive field i is matched, the hash value corresponding to the sensitive field i
Figure 349447DEST_PATH_IMAGE018
A corresponding confidence score;
preferably: the mapping table indicates a preset mapping table;
step S344: calculating a total credibility score of the personal health information to be updated
Figure DEST_PATH_IMAGE019
；
Figure 780559DEST_PATH_IMAGE020
;
Step S345: judging whether all the personal health information to be updated is processed, if so, updating the personal health information record corresponding to the personal health information to be updated with the highest total credibility score by using the received personal health information; otherwise, the combination of the hash values is used as an identification combination, a new health record is created, and the personal health information and the identification combination are stored in a local storage node in an associated manner;
preferably: the identification combinations of different local nodes corresponding to each piece of health information in the remote storage node are calculated and stored in advance, so that the storage efficiency of the remote node is improved;
by the method, the personal health information acquired by the complex data source can be updated in a convergence manner, so that the reliable information is finally completed, the personal health information which is unreliable or has errors is gradually marginalized and finally deleted, and the information optimization of the remote storage node is realized;
preferably: when the total credibility scores of all records are smaller than the threshold value, not storing the received personal health information;
alternatively: when the total credibility scores of all records are smaller than a threshold, deducing different sensitive fields by using sensitive fields with the same personal health information as the highest total credibility score, and if the deduction is successful, covering the different sensitive fields with the deducted data; calculating the total credibility score again based on the covered personal health information, and storing the received personal health information when the total new score is higher than the bottom line threshold value; if the total confidence score is still below the baseline threshold, not storing the received personal health information; for example: when the identity card number and the name are determined, the mobile phone number can be determined in an inference mode, and when the information collected by the local node is wrong, the information is estimated by the remote node with high credibility, so that the information error storage and unnecessary user feedback caused by limited local information are avoided; considering the case that the credit score is not equal to zero and is lower than the bottom line threshold, a new record can be directly created for the case that the total credit score is equal to 0;
for a remote node, the storage identification combination is unnecessary, the possibility of stealing the personal health information in a way of hash value forgery is avoided, only the part of the personal health information uploaded to the remote node is visible to the remote node, and a data barrier is formed between different local nodes; the remote node does not need to carry out data change and desensitization calculation, data desensitization and data barriers to different local nodes are formed through identification combinations obtained through calculation of different hash functions, and meanwhile, the searching efficiency is also improved;
preferably: the bottom line threshold is a preset value;
step S4: the remote node provides a sharing service of the personal health information when receiving a remote access request of the local node; wherein: the request comprises sensitive field and non-sensitive field values;
the step S4 specifically includes the following steps:
step S41: the remote node judges the authority of the remote access request, if the authority is a preset authority, all records of hitting the numerical values of the sensitive field and the non-sensitive field are obtained and returned to the local node;
preferably: sequencing the hit records according to the sequence of the hit number from most to less, and returning the hit records to the local node according to the sequence; if the authority is not the preset authority, entering the next step; here the preset right is a high right; for requests which do not have higher authority, real desensitization needs to be realized through identification combination;
preferably, the following components: the hit is a full hit or a partial hit; all fields related in the request are hit when the full hit occurs, and the partial hit is the partial field hit related in the request;
step S42: acquiring a corresponding hash function according to the identification of the local control node sending the personal health information; specifically, the method comprises the following steps: acquiring an ith hash function corresponding to the ith local control node aiming at each piece of personal health information;
step S43: respectively calculating the hash value of each sensitive field by adopting a hash function; searching a remote storage node based on the hash value to obtain records hitting all sensitive fields as a quasi-target record set;
step S44: searching records of hitting all non-sensitive field values from the quasi-target record set to serve as a target record set;
step S45: returning the target record set as the requested personal health information to the local node; by the mode, a personal health information management mode of local loose sharing and remote trusted sharing is realized; based on the health information providing mode, the personal health information related to the sensitive field is provided only on the basis that the local node already grasps the information contained in the sensitive field, so that desensitization to the personal health information of a new local node not grasped and contained in the sensitive field is realized;
the terms "data processing apparatus", "data processing system", "user equipment" or "computing device" encompass all kinds of apparatus, devices and machines for processing data, including by way of example a programmable processor, a computer, a system on a chip, or a plurality or combination of the above. The apparatus can comprise special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit). The apparatus can include, in addition to hardware, code that creates an execution environment for the computer program, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, a cross-platform execution environment, a virtual machine, or a combination of one or more of the above. The apparatus and execution environment may implement a variety of different computing model infrastructures, such as web services, distributed computing, and grid computing infrastructures.
A computer program (also known as a program, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. The computer program may, but need not, correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, subroutines, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.

Claims (8)

1. A remote digital media sharing method based on 5G internet is characterized by comprising the following steps:
step S1: collecting personal health information;
step S2: the local node manages personal health information and sends the personal health information to the remote node;
the local node performs personal health information management, and specifically comprises the following steps:
step SA 21: determining sensitive fields in the collected personal health information; wherein: the sensitive field is a field capable of personal identification;
step SA 22: acquiring a corresponding hash function according to the identification of the local control node; different local control nodes adopt different hash functions, the different hash functions are predetermined for the current local node and the remote node, and the hash functions of other local nodes for the current local node are unknown;
step SA 23: respectively calculating the hash value of each sensitive field and the specific sensitive field combination by adopting a hash function; wherein: the particular sensitive field combination comprises a plurality of sensitive fields; the specific sensitive field and the specific sensitive field combination are preset;
step SA 24: searching an identification combination of the local storage node by using the hash value of the specific sensitive field or the specific sensitive field combination, if the identification combination is hit, acquiring a corresponding record, and updating the personal health information stored in the local storage node by using the acquired personal health information; otherwise, the combination of the hash values of the sensitive fields is used as an identification combination, a new health record is created, and the personal health information and the identification combination thereof are stored in the local storage node in an associated manner;
the combination hit with the identifier specifically includes: when one or more of the particular sensitive fields, or one or more of the particular combinations of sensitive fields, hit one or more corresponding sensitive fields, or combinations of sensitive fields, of the identified combinations hit;
step S3: the remote node collects and stores the received personal health information to a remote storage node;
the step S3 specifically includes the following steps:
step S31: determining sensitive fields in the personal health information;
step S32: acquiring a corresponding hash function according to the identification of the local control node sending the personal health information; specifically, the method comprises the following steps: acquiring an ith hash function corresponding to the ith local control node;
step S33: respectively calculating the hash value of each sensitive field by adopting an ith hash function; information collection is given up in a sensitive field combination mode when the remote node stores, and the reliability of data collected by the remote node is improved;
step S34: calculating the credibility score of each piece of personal health information, and updating the personal health information record with the highest credibility score by using the received personal health information; wherein i is the number of the local control node;
the method specifically comprises the following steps:
step S341: respectively searching remote storage nodes based on the hash value of each sensitive field to acquire personal health data hit by any hash value as personal health information to be updated;
step S342: acquiring a piece of personal health information to be updated;
step S343: calculating a credibility score corresponding to each hash value through a mapping function; wherein: the trustworthiness score comprises a conditional trustworthiness score and an unconditional trustworthiness score; the mapping function is set in a mapping table mode;
calculating a conditional confidence score corresponding to each hash value
Figure DEST_PATH_IMAGE002
The method specifically comprises the following steps:
Figure DEST_PATH_IMAGE004
；
Figure DEST_PATH_IMAGE006
when the sensitive field i is matched and the sensitive field or the sensitive field set map (i) is matched, the hash value corresponding to the sensitive field i
Figure DEST_PATH_IMAGE007
A corresponding confidence score;
Figure DEST_PATH_IMAGE008
representing the corresponding hash value or hash value set of the sensitive field or sensitive field set map (i);
calculating an unconditional trusted score corresponding to each hash value
Figure DEST_PATH_IMAGE009
The method specifically comprises the following steps:
Figure DEST_PATH_IMAGE011
(ii) a When the sensitive field i is matched, the hash value corresponding to the sensitive field i
Figure 702450DEST_PATH_IMAGE012
A corresponding confidence score;
step S344: calculating a total credibility score of the personal health information to be updated
Figure 416328DEST_PATH_IMAGE014
；
Figure 805852DEST_PATH_IMAGE016
；
Step S345: judging whether all the personal health information to be updated is processed, if so, updating the personal health information record corresponding to the personal health information to be updated with the highest total credibility score by using the received personal health information; otherwise, the combination of the hash values is used as an identification combination, a new health record is created, and the personal health information and the identification combination are stored in the local storage node in an associated manner;
step S4: the remote node provides a sharing service of the personal health information upon receiving a remote access request of the local node.
2. The method as claimed in claim 1, wherein the sensitive fields are identification number, name, diagnosis number, and mobile phone number.
3. The method of claim 2, wherein the specific sensitive fields are combined into one or more fields.
4. A remote digital media sharing system based on 5G internet, the system comprising: a remote node and a local node; the remote nodes comprise a remote control node and a remote storage node; the local nodes comprise local control nodes and local storage nodes; the remote node and the local node communicate with each other through a 5G Internet, and the system performs the 5G Internet-based remote digital media sharing method according to any one of claims 1 to 3.
5. The remote digital media sharing system based on 5G internet as claimed in claim 4, wherein the local node is used to collect personal health information from local and save it in local storage node; the system is also used for transmitting the collected personal health information to a remote node through the 5G internet; the local node is further configured to obtain personal health information from the remote node and provide health services based on the obtained personal health information.
6. A processor, characterized in that the processor is configured to run a program, wherein the program is run to execute the 5G internet-based remote digital media sharing method according to any one of claims 1 to 3.
7. A computer-readable storage medium, characterized by comprising a program which, when run on a computer, causes the computer to execute the 5G internet-based remote digital media sharing method according to any one of claims 1 to 3.
8. An execution device comprising a processor coupled to a memory, the memory storing program instructions that, when executed by the processor, implement the 5G internet-based remote digital media sharing method of any of claims 1-3.
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